The present invention relates to segmentation of anatomical structures in magnetic resonance imaging (MRI) volumes, and more particularly, to 3D segmentation of anatomical structures of the brain in MRI volumes using graph cuts.
The quantitative analysis of anatomical structures, such as the cerebrum, cerebellum, and the brain stem in MRI brain volumes is important in the study and detection of cerebral disease. In particular, volumetric quantification of cerebral and cerebellar tissues is important in image-based assessment of neuroanatomical disorders such as autism and Asperger's syndrome. The segmentation of the anatomical structures of the brain can be difficult due to problems such as lack of boundaries between the anatomical structures, poor contrast in medical images of the brain, and noise in the images, which is mainly attributed to the image acquisition systems (e.g., MRI) and partial volume effects. Accordingly, because of such problems, image segmentation methods such as active contours or region growing are subject to leakage issues and are not reliable. Since a manual delineation of the anatomical brain structures is too time consuming, various techniques have been developed to increase robustness in segmenting anatomical brain structures. These techniques include active contours with shape model prior knowledge, atlas registration, and interactive graph cuts segmentation.
In active contour with shape model prior knowledge techniques, a prior shape constraint is incorporated into the active contour evolution in order to further constrain the segmentation. Shape priors can be modeled by a known class of shapes or through statistical training. These techniques are highly dependent on the selection of an accurate shape prior. Accordingly, the choice of the models for the training or for the class of shapes determines the accuracy of the segmentation.
In atlas registration techniques, combinations of rigid and non-rigid transformations of an atlas are used to aid in detecting the internal structures in an MR image of the brain. For an atlas to be accurate, the atlas typically must be very complex. Although these techniques can be successful, there typically is a high computational cost and it is difficult to construct an accurate atlas. Thus, these techniques can be time consuming and expensive.
In interactive graph cuts techniques, an MRI brain volume is represented as a discrete graph. The graph is generated using vertices representing the image pixels (or voxels), as well as edges connecting the vertices, typically using 6 or 26 neighborhood connectivity. A user marks certain pixels as object or background, which would define the terminals of the graph. Graph cuts are then calculated to determine the segmentation. The quality of the segmentation depends on the number of seeds used in initialization. In this technique, it can be difficult for a user to accurately mark the object and background. In addition, many seeds must be added in order to give a strong spatial constraint for the graph cuts. Accordingly, graph cuts segmentation techniques can lead to erroneous segmentations.